Soil N-Alkane Δd Vs. Altitude Gradients Along Mount Gongga, China

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Geochimica et Cosmochimica Acta 72 (2008) 5165–5174 www.elsevier.com/locate/gca

Soil n-alkane dD vs. altitude gradients along Mount Gongga, China

Guodong Jia *, Kai Wei, Fajin Chen, Ping’an Peng

State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China

Received 10 December 2007; accepted in revised form 6 August 2008; available online 20 August 2008

Abstract

The altitude effect on the isotopic composition of precipitation and its application to paleoelevation reconstruction using authigenic or pedogenic minerals have been intensively studied. However, there are still no studies on variations in biomarker dD along altitude transects to investigate its potential as a paleoelevation indicator, although it has been observed that dDof higher plant lipid may record changes in precipitation dD(dDp). Here, we present dD values of higher plant-derived C27,C29, and C31 n-alkanes from surface soil along the eastern slope of Mount Gongga, China with great changes in physical variables and vegetation over a range from 1000 to 4000 m above sea level. The weighted-mean dD values of these n-alkanes (dDwax) 2 show significant linear correlations with predicted dDp values (R = 0.76) with an apparent isotopic enrichment (ewax–p)of 137 ± 9&, indicating that soil dDwax values track overall dDp variation along the entire altitudinal transect. Leaf dDwax is also highly correlated with mountain altitude by a significant quadratic relationship (R2 = 0.80). Evapotranspiration is found declining with altitude, potentially lowering dDwax values at higher elevations. However, this evapotranspiration effect is believed to be largely compensated by the opposing effect of vegetation changes, resulting in less varied ewax–p values over the slope transect. This study therefore confirms the potential of using leaf dDwax to infer paleoelevations, and more generally, to infer the dD of precipitation. Crown copyright Ó 2008 Published by Elsevier Ltd. All rights reserved.

1. INTRODUCTION bonate, kaolinite, and muscovite that precipitate in equilib- rium with surface waters. Hence paleoelevation of Numerous studies have characterized the relationship mountainous regions, a crucial variable in tectonics and between elevation and the stable isotope composition of climate research, can be reasonably constrained (e.g., Poage precipitation or meteorically derived waters (Ambach and Chamberlain, 2001; Rowley and Garzione, 2007). et al., 1968; Siegenthaler and Oeschger, 1980; Ramesh However, in addition to source water isotopic composi- and Sarin, 1992; Bartarya et al., 1995; Garzione et al., tions, mineral isotopic compositions are influenced by 2000a,b; Poage and Chamberlain, 2001; Gonfiantini et al., many other confounding factors; for example, the tempera- 2001; Dalai et al., 2002). The isotopic values of precipita- ture at which minerals precipitate, the timing of mineral 18 tion (d Op and dDp) decrease as a result of distillation of formation, and diagenesis alteration after mineral forma- 18O and D in precipitation as the air masses move from tion (Poage and Chamberlain, 2001; Morrill and Koch, 18 the oceans to the continents. This decrease of d Op and 2002; Garzione et al., 2004, 2006). These factors introduce dDp values is enhanced as air masses pass over mountains, large uncertainties to paleoelevation reconstructions, sug- resulting in the so-called ‘‘altitude effect”. It has been shown gesting the needs to refine the reconstructions by multipr- that the altitude effect could be well documented in d18O oxy studies. Consequently, investigation of new proxies and/or dD of authigenic or pedogenic minerals, such as car- for paleoelevation is of great value. Compound-specific hydrogen isotope ratios of plant- derived lipids are emerging as a new paleoclimatic and * Corresponding author. Fax: +86 (20)85290706. paleohydrological proxy. Although the implicit assumption E-mail address: [email protected] (G. Jia). of nearly constant biological fractionation in many works

0016-7037/$ - see front matter Crown copyright Ó 2008 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.gca.2008.08.004 5166 G. Jia et al. / Geochimica et Cosmochimica Acta 72 (2008) 5165–5174 has been suggested not strictly true (Sessions, 2006; Hou ical zonations (Thomas, 1999; Zhong et al., 1999)(Fig. 1). et al., 2007), making it difficult to establish quantitative The eastern slope is covered by dense clouds and fogs dur- environmental proxies based on lipid dD, surveys of mod- ing late spring and summer resulting in attenuation of solar ern environments spanning a range of environmental condi- radiation. Mean annual precipitation generally increases tions for establishing the relationship between lipid dD and with altitude, while evaporation is reverse, resulting in environmental water (Sauer et al., 2001; Huang et al., 2002, RH increase with elevation and positive water balance (pre- 2004; Yang and Huang, 2003; Sachse et al., 2004, 2006; cipitation–evaporation, P–E) above 2700 m. Mean annual Smith and Freeman, 2006; Hou et al., 2008) were still of sig- temperature declines upward from 11.8 °C at 1600 m asl to nificance, as indicated by successful applications of lipid dD 3.4 °C at 3000 m asl. Over a vertical range of 3800 m from to many paleoenvironmental studies that often use multipr- the subtropical arid Dadu River valley to the snowline, an oxies to corroborate each other (e.g., Huang et al., 2002; intact, continuous vertical vegetation spectrum from the Hou et al., 2006; Pagani et al., 2006; Jacob et al., 2007). subtropical zone to the frigid zone can be observed The correlations between organic dD and source water (Fig. 1). Mount Gongga thereby is an area with very high dD were found to vary significantly between different stud- biodiversity: about 2500 plant species belonging to 869 gen- ies, but the linear fits between the two variables still ac- era and 185 families have been identified (Thomas, 1999). counted for most of the observed variances suggested by Surface soil samples (0–5 cm) in A horizon for this study the coefficient of determination (R2) larger than 0.6 (Huang were collected after removing the litter layer along the slope et al., 2002; Sessions and Hayes, 2005; Hou et al., 2008). A transect of Mount Gongga over 1-week period in late May, high correlation between leaf lipid dD and precipitation dD 2004. According to Wang et al. (2005), all samples from A along a large natural transect with marked changes in cli- horizon of soil along the eastern slope of Mount Gongga mate and vegetation has recently been attributed to the off- have D14C values greater than zero, suggesting A horizons set of the opposing isotopic effects of relative humidity contain a substantial amount of bomb carbon and have (RH) and vegetation (Hou et al., 2008). Therefore, leaf lipid ages of about only several decades. Therefore, vegetation dD has the potential to record changes in the isotopic com- and moisture source can be assumed constant for sampled position of the hydrogen source at different climate settings, soil lipid signals. The altitude of each locality was deter- and further, to be used as paleoelevation proxy. However, mined using a handheld GPS unit with an error of in addition to common environmental parameters such as ±10 m. Samples at each locality were the mixture of three precipitation, evaporation, RH, and vegetation along a nat- subsamples randomly taken within a radius of 10 m, using ural horizontal transect, the mountain environment along a small metal scoop. All samples were wet, and each of an altitudinal transect has its own particularities, e.g., them was tightly sealed on site in a polyethylene zipper decreasing pressures of air, moisture, and CO2 with eleva- bag. The first bag was then sealed in a second, ‘‘outer”, zip- tions, that are crucial to plant transpiration and hence to per bag to insure against possible damage and leakage. isotopic fractionation. Therefore, the feasibility of using Bagged samples were frozen immediately after being carried leaf lipid dD in paleosols or sediments to infer paleoeleva- in the laboratory. The duration between sampling in the tions remains unclear and is pending to be answered. Here, field and freezing in the laboratory was about 2 weeks. dD data of higher plant-derived n-alkanes extracted from soil samples at various altitudes on Mount Gongga, China 2.2. Analysis of soil water isotopic values are presented in order to assess the potential of lipid dDasa paleoelevation proxy. In order to obtain both soil waters and lipids, we referred to an existing method which uses a dense, water- 2. STUDY AREA, SAMPLING, AND EXPERIMENTS immiscible liquid to displace soil solution during high speed centrifugation (Litaor, 1988; and references therein). 2.1. The altitudinal transect and samples We chose dichloromethane (DCM) as both the water- immiscible liquid and lipid extractant. Briefly, a thawed soil Mount Gongga, located approximately 30°N, 102°E sample was divided into several subsamples, and each sub- (Fig. 1) on the eastern side of the Tibetan Plateau in Sich- sample was immersed in DCM in a 50-mL Teflon tube for uan Province, southwest China, is the highest part (peak ultrasonic extraction for 10 min. Then phase separation was at 7556 m above sea level, asl) of the north-south trending performed by centrifugation at 6000 rpm, resulting in three mountain ranges collectively known as Daxueshan. The phases from the top to the bottom: water, DCM, and soil. eastern slope of Mount Gongga reaches down into the deep Subsequently, water and DCM were taken out carefully in Dadu River valley at 1100 m asl with a horizontal distance turn by pipettes. Usually after three times of extraction and of less than 30 km, and the western slope blends into the centrifugation, water content was too low to separate from eastern Tibetan Plateau at 3000 to 3500 m (Thomas, DCM. But for complete lipid extraction the procedure was 1997, 1999). Regional climate shows typical monsoon pat- repeated five times for each subsample. About 10-mL soil tern of temperature, precipitation, and evaporation. The water could be finally obtained for each sample which hottest months occur well within the rainy season from was enough for oxygen and hydrogen isotope analysis. May to September while evaporation peaks in the sunny Before isotopic analysis, soil waters were put through a pre-monsoon months (Fig. 1; Thomas, 1997, 1999). column packed with active carbon to eliminate possible Changes in climate, vegetation, and soils are prominent traces of DCM. Then water–zinc reaction (at 400 °C) and over the slope transect and have created vertical geoecolog- water–CO2 equilibration (at 25 °C) methods were used to Altitudinal changes of lipid hydrogen isotope 5167

Fig. 1. The vertical vegetation zonation and climatic conditions on the eastern slope of Mount Gongga, according to Zhong et al. (1999) and Thomas (1997). AT, annual temperature; AP, annual precipitation; AE, annual evaporation. Main plant genera for the vertical vegetation zones are as follows: Z1: Dioscorea, Setaria, and Bidens; Z2: Quercus, Cyclobalanopsis, and Rubus; Z3: Acer, Lithocarpus, and Rhododendron; Z4: Acer, Pinus, and Picea; Z5: Abies, Picea, and Betula; Z6: Rhododendron, Carex, and Primula; Z7: Rhodiola, Pedicularis, and Kobresia.

collect hydrogen gas (H2) and CO2, respectively. H2 and was then introduced into the mass spectrometer. The tem- CO2 were finally analyzed for their hydrogen and oxygen perature program and capillary column were identical to isotope ratios on a Finnigan MAT 253 isotope ratio mass those used for GC analysis. The H3 factor for the mass spectrometer (IRMS). The isotopic composition was ex- spectrometer was determined every six injections using the pressed in d per mil, i.e., deviation & of the isotope ratios standard hydrogen gas introduced through the interface, D/H and 18O/16O from the reference V-SMOW. The preci- and its average value was 6.5 ± 0.1 ppm/nA during our sion for replicates of samples was ±0.2& for oxygen isoto- sample analysis. The reproducibility and accuracy of the pic analysis, and ±2& for D/H analysis. hydrogen isotopic analyses were evaluated routinely using GC–IRMS reference materials containing nine n-alkane 2.3. Analysis of soil n-alkane dD values homologues (C12,C14,C16,C18,C20,C25,C28,C30,C32) provided by Indiana University. Typically, during the anal- The total extract in DCM phase was dehydrated by yses of unknown samples, laboratory standards were anhydrous sodium sulfate, and then separated into polarity injected periodically (typically one standard injection per fractions using short columns filled with approximately 2 g six sample analyses) to ensure that the mass spectrometer of activated silica gel (70–230 mesh). The hydrocarbon frac- were stable. dD values of sample compounds were referenced tion was eluted using hexane (10 mL), and then purified for to gaseous hydrogen which had been calibrated against the n-alkanes using urea adduction. n-Alkanes were identified Indiana standard, independently calibrated against by comparison of retention times defined by gas-chroma- V-SMOW-water. During our analysis, the dD values of tography (GC) analysis of a mixed n-alkane standards. standard compounds varied relatively small (1r <3&, The GC analysis was conducted using a Hewlett–Packard n = 18). The standard deviations of triplicate analysis for soil 6890 gas chromatograph with flame ionization detector n-alkanes ranged from 0& to 10& (Table 1). (FID). The injector temperature was maintained at 290 °C, with a detector temperature of 300 °C. The GC 3. RESULTS AND DISCUSSION oven program increased from 60 °C (held for 1 min) to 300 °Cat6°C/min, and was held at 300 °C for 20 min. 3.1. Predicted precipitation isotopes along the altitude Hydrogen isotopic analyses of individual n-alkanes via transect GC–TC–IRMS utilized an HP-6890 GC and a high-temperature pyrolysis unit that was connected on-line via a In order to compare with our soil water and n-alkane 18 GCC III interface to a Finnigan MAT Delta Plus XL isotopic results, the precipitation isotopic values (d Op IRMS. Individual compounds separated by GC were pyrol- and dDp) along the altitudinal transect of Mount Gongga ysed to convert organic H into H2 at 1450 °C, and the H2 were calculated using Rowley’s fourth-order polynomial 5168 G. Jia et al. / Geochimica et Cosmochimica Acta 72 (2008) 5165–5174

Table 1 Data for soil waters and n-alkanes and the calculated precipitation dD along the eastern slope of Mount Gongga c Altitude (m) Soil water Soil n-alkane dDp Relative Conc. (%) 18 a a a b d Osw dDsw dDC27 SD dDC29 SD dDC31 SD dDwax nC27 nC29 nC31 1180 175 ± 1 178 ± 1 193 ± 0 185 56 14.5 32.8 52.7 1220 187 ± 0 195 ± 1 193 ± 2 192 57 25.8 34.9 39.3 1320 1.5 10 178 ± 1 180 ± 4 177 ± 2 178 58 8.1 30.5 61.4 1375 0.3 6 169 ± 0 169 ± 0 172 ± 0 170 59 19.5 35.2 45.3 1440 1.5 18 191 ± 0 195 ± 0 206 ± 7 200 60 12.3 38.7 49.0 1470 0.8 9 182 ± 2 184 ± 3 196 ± 0 188 60 34.4 29.5 36.1 1515 184 ± 1 178 ± 0 176 ± 2 180 60 36.5 48.4 15.1 1550 1.3 11 183 ± 3 197 ± 2 193 61 9.6 28.3 62.1 1610 189 ± 0 192 ± 0 191 ± 1 191 62 39.0 39.1 21.9 1645 2.2 13 183 ± 10 181 ± 3 182 ± 1 182 62 21.7 32.7 45.6 1700 2.1 16 193 ± 2 193 ± 2 194 ± 2 194 63 12.5 28.5 59.0 1740 4.1 28 181 ± 6 178 ± 2 183 ± 3 181 63 25.4 37.0 37.7 1800 2.8 26 200 ± 1 202 ± 1 196 ± 0 199 64 30.6 34.7 34.6 1850 0.5 18 187 ± 1 195 ± 2 196 ± 0 194 65 12.9 45.0 42.1 1915 2.2 26 191 ± 3 188 ± 6 186 ± 3 188 66 24.2 27.0 48.9 1973 3.6 30 189 ± 4 201 ± 1 197 ± 1 198 66 14.5 52.0 33.5 2005 5.8 37 189 ± 1 199 ± 2 191 ± 2 193 67 23.5 36.2 40.3 2085 5.7 44 194 ± 5 206 ± 0 194 ± 1 199 68 24.9 40.7 34.4 2115 7 41 187 ± 2 185 ± 4 187 ± 1 186 68 15.2 42.4 42.4 2160 4.5 24 204 ± 1 214 ± 2 203 ± 3 208 69 20.6 47.0 32.4 2220 5.8 46 196 ± 4 203 ± 2 188 ± 4 197 70 20.2 48.7 31.1 2300 3.2 26 191 ± 1 195 ± 2 195 ± 3 194 71 21.9 57.8 20.3 2350 1.9 22 186 ± 1 189 ± 1 182 ± 1 187 72 35.5 56.9 7.6 2420 3.6 39 208 ± 1 197 ± 1 191 ± 1 197 73 24.3 33.8 41.9 2470 4.7 25 73 2540 2.5 19 210 ± 1 200 ± 1 204 ± 4 204 75 24.7 42.6 32.7 2620 5.8 39 192 ± 1 199 ± 0 194 ± 2 196 76 30.0 43.0 27.0 2742 3.7 31 195 ± 4 206 ± 8 216 ± 0 207 78 28.5 35.7 35.8 2764 5.5 37 206 ± 6 207 ± 2 200 ± 4 205 78 19.6 55.6 24.9 2808 4.2 37 203 ± 7 193 ± 4 199 ± 1 198 79 24.0 36.3 39.7 2846 5.8 40 197 ± 3 196 ± 2 202 ± 2 199 79 22.1 40.7 37.2 2920 7.5 54 205 ± 7 197 ± 3 205 ± 3 202 81 23.6 38.6 37.9 2960 7.8 52 198 ± 1 198 ± 1 199 ± 2 199 81 21.9 44.6 33.5 3015 9.3 62 202 ± 1 196 ± 2 208 ± 2 202 82 25.5 39.1 35.4 3049 4.8 32 212 ± 3 207 ± 3 218 ± 1 213 83 17.6 41.7 40.7 3065 8.8 64 215 ± 1 207 ± 1 211 ± 1 210 83 25.4 41.5 33.1 3119 8.7 58 221 ± 1 217 ± 1 219 ± 0 219 84 34.5 28.1 37.5 3145 5.9 45 203 ± 0 195 ± 1 206 ± 2 201 85 29.9 33.6 36.4 3188 5.6 30 226 ± 5 220 ± 1 229 ± 2 224 85 15.9 43.4 40.7 3209 2.3 25 219 ± 3 200 ± 8 217 ± 1 212 86 22.7 30.9 46.4 3518 7.2 46 234 ± 0 224 ± 0 229 ± 1 228 92 9.5 16.5 73.9 3596 7.1 51 232 ± 1 219 ± 0 217 ± 3 220 93 14.1 21.2 64.7 3676 9.4 55 230 ± 1 241 ± 0 236 ± 0 236 95 21.9 25.4 52.7 3769 11 87 241 ± 2 237 ± 0 245 ± 1 241 97 18.0 33.6 48.4 3819 11.6 98 242 ± 1 240 ± 1 235 ± 1 237 98 13.5 26.8 59.7 a Standard deviation of triplicate analysis. b Weighted-mean dD values of C27,C29,C31 n-alkanes. c Predicted precipitation dD along the altitudinal transect.

18 regression of the relationship of D(d Op) vs. elevation. This (30.6°N, 104.2°E; 506 m asl), was selected as the reference regression is derived from modeling all possible modern ini- low-altitude locality. tial sea level temperature and relative humidity pairs for The rainy-season moisture source for the eastern slope 18 values of D(d Op) between 0& and 25& (Rowley, of Mount Gongga is believed to be the same as that for 18 2007). In the relationship, D(d Op) is the diﬀerences be- Chengdu, which is originated from low latitude western Pa- 18 tween the d Op at high- and low-altitude localities. In this ciﬁc and Indian Oceans (Thomas, 1997; Tian et al., 2007). paper, the nearest station of IAEA Global Network for Iso- The monthly isotopic data of precipitation for Chengdu topes in Precipitation (GNIP) to Mount Gongga, Chengdu in the GNIP database span 1986–1997 (IAEA/WMO, Altitudinal changes of lipid hydrogen isotope 5169

2006). However, dry-season data are available only for part of this period. For instance, there is only one or two monitoring data for each month from October to February; moreover, they are distributed in different years. The low or lack of precipitation from October to February might be the cause of the partial years of monitoring. Moreover, the moisture source for these low-precipitation months has been thought to be from recycled continental water (Tian et al., 2007). Therefore, we did not use the sparse data from October to February, but calculated amount-weighted monthly mean isotopic values for each month only from March to September. And then a LMWL was constructed using these monthly mean data, which gives a slope of 7.53 (Fig. 2). The multi-year weighted-mean isotopic values Fig. 3. Predicted precipitation dD vs. altitude for the slope transect for March–September are 6.7& for d18O and 49.6& for of Mount Gongga. Dashed curves define the ±1r uncertainties for dD, which could be viewed as annual weighted-mean values the prediction. Two precipitation dD data (crosses) at nearby sites because of the overwhelming amount of precipitation dur- reported by Xu et al. (2006) and Yang et al. (2007), respectively, are ing the period (>90% annual amount). given for comparison. See related interpretations in the text. The obtained weighted-mean d18O values of 6.70& was used thereupon as our low-altitude reference in calcu- 18 lating altitudinal changes of d Op along Mount Gongga. dDsw between 98& and 6&. The two isotopes display 18 The predicted d Op for every altitude and their ±1r uncer- a linear relationship as shown in Fig. 2 tainties were inversely calculated using the fourth-order 18 dDsw ¼ð5:76 0:37Þd O sw ð9:12 2:08Þ polynomial regressions given by Rowley (2007). Then dDp along the altitudinal transect was calculated according to ðn ¼ 41; R2 ¼ 0:87; p < 0:001Þ the predicted d18O and LMWL (Table 1), as illustrated p In the linear fit, the slope is obviously lower than the slope in Fig. 3. For comparison, published dD data (Xu et al., p value of 8 for Craig’s ‘‘global meteoric water line 2006; Yang et al., 2007) from two nearby sites north to (GMWL)” (Craig, 1961) and of 7.53 for LMWL obtained Mount Gongga are put in the plot, indicating the validity above. The slope of the relationship between d18O and of the prediction. Although the two sites, one is at dD is often used to identify whether or not the water has 102°580E, 30°510N, 2805 m asl and the other at 103°490E, been subjected to evaporation. Using Craig and Gordon’s 32°430N, 3588 m asl, are not on the Mount Gongga, they model (1965), slopes between 4 and 6 were predicted for are in the same north-south trending mountain ranges evaporation of rainwater under conditions encountered in and the same climatic region as Mount Gongga. Therefore, most climatic regimes (Gat, 1971). The lower slopes for soil we think the comparison is feasible. In the following text, and river waters were commonly observed and were predicted dD will be used for comparison with our soil p thought to be caused by evaporation (Barnes and Allison, n-alkane dD. 1983; Ramesh and Sarin, 1992; Dalai et al., 2002). Thus the obtained relationship between d18O and dD here 3.2. Soil water oxygen and hydrogen isotopes (d18O and sw sw sw indicates that the sampled soil waters on the eastern slope dD ) sw of Mount Gongga have undergone some evaporation loss. 18 18 Evaporation could be a fatal factor influencing d Osw Data of d Osw and dDsw for soil samples are shown in 18 and dDsw lapse rates. In Fig. 4a and b, the plots of Table 1. d Osw ranges between 11.6& and +1.5&, and 18 d Osw and dDsw vs. altitude (h, in km), respectively, are shown. Regression analysis of the data gives the following best-fit lines:

18 d Osw ¼ð3:7 0:4Þh þð4:5 1:1Þ ðn ¼ 41; R2 ¼ 0:66; p < 0:01Þ

dDsw ¼ð22:0 2:7Þh þð18:9 7:1Þ ðn ¼ 41; R2 ¼ 0:63; p < 0:001Þ The slopes of their best-ﬁt lines indicate isotopic lapse 1 18 1 rates of 0.37& 100 m for d Osw and 2.2& 100 m 18 for dDsw. The d Osw lapse rate here is more negative than ‘‘global isotopic lapse rate” of 0.28& 100 m1 obtained from precipitates and river waters by Poage and Chamber- lain (2001), although it is still well within the scattered values ranging from 0.1& to 0.5& 100 m1 in the Fig. 2. Correlation between soil water dD and d18O. The dashed literature (Siegenthaler and Oeschger, 1980; Ramesh and line is the local meteoric water line (LMWL) for Chengdu. Sarin, 1992; Bartarya et al., 1995; Garzione et al., 2000b; 5170 G. Jia et al. / Geochimica et Cosmochimica Acta 72 (2008) 5165–5174

weight n-alkanes reflect the contributions of unaltered leaf waxes of higher plants to the soil organic matter (Eglinton and Hamilton, 1967). Another useful parameter from n-alkane distribution is the average chain length (ACL) that has been suggested to change with plant types. Woody plants display n-alkane distributions dominated by the C27 or C29 n-alkanes, whereas grasses have distributions dominated by the C31 n-alkane (Cranwell, 1973; Maffei, 1996). Therefore, grasses are usually high in ACL values relatively to trees. In our results, a plot of ACL27–31 vs. altitude (Fig. 5) shows a quadratic relationship between them with lower ACL27–31 values in the mid altitude and higher values towards the low and high altitudes. This pattern is quite consistent with the vegetation distributions along the slope transect (Fig. 1) with shrubs and grasses dominant below 1500 m and above 3500 m asl, respectively. Compound-specific dD values were determined for the leaf wax n-alkanes (nC27–31) from the soils along the eastern slope of Mount Gongga (Table 1). These n-alkanes exhibit isotopic signatures in comparable ranges (242& to 169& for nC27, 241& to 169& for nC29, and 245& to 172& for nC31) and strong inter-correlations 2 2 (R = 0.82 for nC27 and nC29, R = 0.82 for nC27 and nC31; n = 43, p < 0.001), suggesting a common source for them. Because the mean dD values of the leaf wax n-alkanes could integrate possible species-specific apparent fractionations due to different degrees of leaf-water isotope enrichment (Hou et al., 2007), we think that the weighted-mean Fig. 4. Altitudinal changes in soil water d18O (a) and dD (b) along dD value from the leaf wax n-alkanes (nC27/29/31), dDwax, the slope transect of Mount Gongga. is more suitable to represent the mean isotope signal of local precipitation, being modified by leaf water evapotranspiration. Thus in the following discussion dDwax is used Gonfiantini et al., 2001; Dalai et al., 2002). Data on dD instead of individual n-alkane dD values. 1 In order to investigate the fidelity of higher plant dD lapse rate were reported between 1& and 4& 100 m wax in the literature (Ramesh and Sarin, 1992; Araguas-Ara- as a recorder of precipitation dD, we compare our soil dDwax values with predicted dDp values and find a high lin- guas et al., 2000), and recently the value of 1.7& 2 2 100 m1 was used by Mulch et al. (2006) to reconstruct ear (R = 0.76) or quadratic (R = 0.81) correlations be- the paleoelevation of Sierra Nevada. We think that the tween them (Fig. 6a). The high correlation is also slightly more negative soil water isotopic lapse rates in this indicated by the less varied apparent isotopic enrichments study could be resulted from differential evaporation, that between local precipitation and soil leaf waxes (ewax–p) could be explained qualitatively as follows: on the eastern (137.4 ± 9.3&). Because organic hydrogen is ultimately slope of Mount Gongga, the changes in water balance from originated from precipitation, this correlation could be P < E to P > E with altitude (Fig. 1) could give rise to less expected. However, the strength of the expected correla- isotopic enrichment of soil water at a higher point than at a tions could be compromised by a number of factors, such lower point, and consequently produce a secondary isotopic lapse rate that is added to the primary isotopic lapse rate of precipitation, resulting in a steeper apparent lapse rate. Accordingly, the influence of changes in water balance with altitude on the isotopic lapse rate should be given more re- gard in paleoelevation reconstructions, as has been re- minded by Quade et al. (2007).

3.3. Leaf n-alkane hydrogen isotope as a proxy for altitude

High-molecular weight n-alkanes in soil samples range from nC23 to nC33, with the most abundant being nC27, nC29, and nC31. A predominance of odd carbon numbered compounds is exhibited clearly as indicated by the carbon preference index (CPI25–33) varying between 5.5 and 13.4 Fig. 5. Altitudinal variations of ACL27–31. ACL27–31 is calculated with a mean value of 8.7. These features of high-molecular from weighted-mean chain length of C27,C29, and C31 n-alkanes. Altitudinal changes of lipid hydrogen isotope 5171 as relative humidity (RH) that could impact dDwax through Our results hence confirm the potential of higher plant-de- changing soil and leaf water dD, and different plant types rived n-alkane dDwax as a proxy for mountain altitude. having distinct isotope enrichment factors (with grasses displaying 40& to 50& lower dD values than trees; Hou 3.4. Impact of environmental factors on ewax–p et al., 2007). Across the eastern slope of Mount Gongga, as introduced previously, both RH and plant types change Large variations in ewax–p have been observed for a vari- significantly. However, they apparently do not exert great ety of plant material in different environmental settings, and influence on the fidelity of dDwax as a recorder of dDP, sug- many environmental variables ranging from vegetation gesting that the combined impact of environmental factors types (e.g., grasses vs. trees, C3 vs. C4 plants) to climate (e.g., vegetation cover, RH) is relatively small. Similar find- parameters (e.g., evaporation and RH) were attributed to ing has also been reported recently by Hou et al. (2008) in a the variations (Chikaraishi and Naraoka, 2003; Sachse study of natural factors affecting sedimentary dDwax in 32 et al., 2006; Sessions, 2006; Smith and Freeman, 2006; lake surface sediments across large gradients of precipita- Hou et al., 2007; Hou et al., 2008; Mu¨gler et al., 2008). tion, relative humidity, and vegetation composition in the Along the eastern slope of Mount Gongga, these environ- southwestern United States. mental factors change remarkably with altitude. Surpris- Since soil dDwax on the eastern slope of Mount Gongga ingly, our mean ewax–p value, 137.4 ± 9.3&, is less well record the altitude-effected dDp, application of soil varied and quite similar to the mean value of dDwax as a proxy for altitude is thus prospective. There is 133 ± 16& for terrestrial plants synthesized by Mu¨gler likewise a high linear correlation of dDwax with altitude et al. (2008), suggesting an overall small combined effect 2 (R = 0.73, Fig. 6b) from our results. Moreover, a sec- of environmental factors on ewax–p (Hou et al., 2008). A ond-order polynomial regression provides an overall better close examination on the altitudinal changes in ewax–p 2 fit for the dDwax and altitudes (R = 0.80, Fig. 6b) than (Fig. 7) reveals that an offset of environmental factor effects does the linear regression. Better fits from second-order occurs below the transition from the belt of dark coniferous polynomials have also been observed for the relationship forest (Z5 in Fig. 1) to that of shrub and meadow (Z6 in of surface water isotopic values vs. altitude, and were con- Fig. 1) at around 3500 m as suggested by the uncorrelation sidered to be consistent with a Rayleigh-type fractionation of ewax–p with altitude. However, above the transition, there process predicting that dDp will become increasingly more appears a decreasing trend in ewax–p values with altitude, negative with each rainout event (Garzione et al., 2000b). implying that the balance of environmental effects on e wax–p is broken at these higher altitudes. In the following, we will give a qualitative evaluation of related environmental variables on ewax–p along the study transect. In addition to precipitation, evaporation, and RH, atmospheric pressure (pa) and partial pressure of CO2 (pCO2) are important variables when investigating plant water balance along an elevation transect. Plant transpiration at higher elevations might be potentially more severe, even though plant temperatures may be considerably lower, due to higher evaporative demand at lower pa (Smith and Geller, 1979; Leuschner, 2000) and/or higher plant stomatal density at lower pCO2 (Woodward, 1987). However, earlier work has shown that the low ambient pCO2 under isother- mal conditions may be nearly compensated by substantial increases in the diffusion coefficient for CO2 at low pa (Gale, 1972). Moreover, recent experiment revealed that leaf lipid

Fig. 6. Correlations of n-alkane dDwax with predicted dDp (a) and Fig. 7. Altitudinal changes in apparent fractionation factor altitude (b). between n-alkane and precipitation (ewax–p). 5172 G. Jia et al. / Geochimica et Cosmochimica Acta 72 (2008) 5165–5174 dD values of thirteen plant species grown in chambers are trial lipid dD values display systematic decreasing trends unaffected by a twofold change in ambient pCO2 level with altitude, faithfully tracking the altitude effect on (Liu and Huang, 2008). The authors hypothesized that the precipitation dD variations. Just as d18O and dDof the effect of enhanced evapotranspiration at lower CO2 is pedogenic minerals forming from soil water could be accompanied by an increase in kinetic hydrogen isotopic used for paleoelevation reconstructions, our findings pres- fractionation due to lower rate of plant growth, and vice ent the prospect of higher plant n-alkane dD as a proxy versa. Therefore, we suppose here that the effect of pCO2 for paleoelevation reconstructions. Compared to mineral 18 on ewax–p could be neglected. As to the effect of pa on plant d O and dD method, organic dD has several strong transpiration, and hence on ewax–p, it has been found that points. For example, because sediments that preserve or- high transpiration rates at high elevations due to lowered ganic material do not typically contain carbonates, organ- pa are expected in mountains of the dry tropics with low ic dD method would allow us to determine elevations at cloudiness and low rainfall but high radiation, but not of locations with only organic material. Secondly, plant lipids the mid-latitudes usually with high rainfall. The occurrence production is quite a short-term cycle (at most several years of high precipitation, low evaporation, and high cloud cov- for evergreen plants) relative to the formation of authigenic er upslope Mount Gongga thereby appears to impede the minerals on the order of 105 years (Poage and Chamberlain, low pa-induced increase in transpiration. To validate this 2001), making it possible for higher-resolution paleoeleva- inference, we estimated the potential transpiration using tion reconstruction. Finally, hydrogen isotopic fraction- the leaf energy balance model developed by Leuschner ation between water and plant lipids during lipid (2000). In the estimation, some site-specific elevation-re- production is less temperature dependent; hence uncertain- lated parameters during plant growing season (May–Sep- ties from temperature effect, needing to be taken into ac- tember), such as air temperature, RH, cloudiness, and count when using mineral isotope proxies (Poage and wind speed, were determined according to their lapse rates, Chamberlain, 2001; Ghosh et al., 2006), could be largely respectively, which were simply calculated by datasets from avoided by n-alkane dD-based estimation. the only two stations at 1600 and 3000 m asl, respectively, Nevertheless, no paleoenvironmental proxy is perfect. on the slope. Our simulation showed a monotonic reduc- For compound-specific dD an important factor needed tion in leaf transpiration by roughly 44% from 1100 to to be evaluated is the possible physical and vegetation 4800 m asl for the slope transect. influences on ewax–p along an altitudinal transect. Only Reduction in leaf transpiration, as well as in soil evapo- when ewax–p is affirmed to be less elevation-dependent ration discussed earlier, are expected to lower ewax–p values or changes in ewax–p can be estimated, can the uncertain- along the altitudinal transect. However, this expectation ties for reconstruction of paleo-precipitation dD, and does not appear, suggesting that other factors compensate hence of paleoelevation, be minimized. From our results, the evapotranspiration effect on ewax–p. Here, we speculate ewax–p is less varied along the altitudinal transect and that changes in plant types with elevation play an important quite similar to the mean values for terrestrial plants, lar- role. Recent studies have shown that ewax–p differs greatly gely due to the offset between the opposing physical and among different types of higher plants (Liu et al., 2006; vegetation effects. This finding is quite similar to that re- Hou et al., 2007), with grasses displaying 40& to 50& ported by Hou et al. (2008), who attributed the high cor- lower dD values than trees. Grasses are a ubiquitous com- relation between higher plant lipid dD and precipitation ponent in the vegetation system on Mount Gongga, with dD along a large natural transect in the southwestern high contributions at the lower mountain belt of arid valley US to the compensation between the two opposing influ- shrub and grass (1000–1600 m asl) and the subalpine–alpine ences of RH and vegetation changes. These similar find- belt of shrub and meadow (>3500 m asl) (Fig. 1). This pat- ings from different settings thereby suggest that the tern of plant type distribution is roughly depicted by the combined effect of physical variables and vegetation types plot of ACL27–31 vs. altitude (Fig. 5), owing to relatively can possibly produce a relatively less varied ewax–p in large ACL27–31 values for gasses. The upslope decreasing most situations. This is understandable because the com- grass contribution below the transition from the belt of binations between physical variables and vegetation types dark coniferous forest to that of shrub and meadow is lar- are not at random, and vegetation changes is predomi- gely coincident with our estimated decline in potential tran- nantly controlled by environmental parameters, with rela- spiration, whereas grass contribution apparently increases tive proportions of trees vs. grasses usually highly above the transition where potential transpiration keeps correlated with physical variables. Nevertheless, the phys- declining. This scenario allows us to believe that the evapo- ical and vegetation effects appear not always perfectly transpiration and vegetation effects on ewax–p largely offset combined to compensate each other, as indicated by the each other below the transition, which leads to less varied decreasing ewax–p values in the subalpine–alpine belt ewax–p along the slope (Fig. 7). Above the transition, the in- above 3500 m in our results (Fig. 7), which remind us crease of grass contribution may help reinforce the effect of the importance of investigation on vegetation and envi- declining transpiration, and hence lowers ewax–p (Fig. 7). ronmental changes when interpreting lipid dD records.

4. IMPLICATIONS AND CONCLUSIONS ACKNOWLEDGMENTS

By analyzing dD of higher plant-derived n-alkanes along We thank Drs. Mingzhong Ren, Tongyang Li, and Wei Li for the eastern slope of Mount Gongga, we found that terres- collaboration in collecting soil samples. We also thank Wanglu Jia Altitudinal changes of lipid hydrogen isotope 5173 for technical support in the State Key Lab of Organic Geochemis- Hou J. Z., Huang Y. S., Wang Y., Shuman B., Oswald W. W., try, Guangzhou Institute of Geochemistry. Dirk Sachse and an Faison E. and Foster D. R. (2006) Postglacial climate recon- anonymous reviewer are sincerely appreciated for their critical re- struction based on compound-specific D/H ratios of fatty acids views that greatly improved this paper. This study is supported from Blood Pond, New England. Geochem. Geophys. Geosyst. by Chinese Academy of Sciences (Grant KZCX3-SW-152-3). 7, Q03008. doi:10.1029/2005GC001076. Hou J. Z., D’Andrea W. J., MacDonald D. and Huang Y. S. 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